This invention relates generally to character correction systems and methods to provide fast and efficient correction of inputted characters. More particularly, the present invention relates to a method for predicting and correcting for character errors on error prone input devices with limited keypads. Characters may include text using Roman based alphabets, Chinese alphabet, Arabic scripts, or virtually any known language's symbology.
In today's increasingly mobile population, the ability to input text into a mobile device is becoming more desirable. Emails, appointments and text messages are routinely inputted into mobile devices, including Personal Digital Assistants (PDA's), cell phones and computerized organizers.
For the business person, the ability to send emails and document appointments, while on the go, enables a jumpstart into the workday, increased productivity and enhanced flexibility. For the teenager, or other casual user, text messaging has become an exceedingly common phenomena and a form of social currency. However, the small, highly portable size of the devices that enable mobile text connectivity also render the text highly error prone due to small, and often ambiguous, keypads for the inputting of text.
Text errors in casual text conversations may be tolerated in some instances; however, when the message is sensitive as to content or recipient, errors may have disastrous effects. As such, there is a strong felt need for text error correction.
This need for text error correction has been evident since the development of word processing. In response, spell check programs have evolved to address the need for error free text. Current text correction, however, requires a lot of resources in terms of processor, power and storage requirements. Moreover, the current text correction may not specifically address the unique problems commonly incurred by authors using mobile devices. These current methods are not particularly efficient and rely upon the availability of free processor resources to check each word, one at a time, against comparables in a dictionary stored in memory. In simple applications, the spell checking activity may be deferred until invoked by the user. In more sophisticated schemes, provided that the activity does not detract from the system response time, words may be evaluated as soon as they can be distinguished, for example as soon as a delimiting character is detected following a word. Similarly, where a character sequence does not match any word in the stored dictionary, it may be possible to compare to near alternatives where one or more characters differ. Simple errors such as capitalization can sometimes be corrected “on the fly” but in technical material, automated correction rapidly becomes infuriating.
Additionally, current text correction often is unable to discern intended text from correctly spelled but erroneous text. Efforts at contextual recognition are still relatively primitive and suffer from “cultural corruption” wherein seemingly identical languages such as British English and American English use quite diverse and sometimes disparate meanings. For example the word “bomb” when used to describe an event may relate to being very good in the British English form yet be indicative of failure in the American English form.
Mobile devices typically have fewer processing, power and storage resources available than a stationary computer system. Additionally, due to specific geometries of input keypads, necessitated by the devices small design, error types and frequencies may be statistically skewed.
Thus, in the typical mobile device, the current text correction technologies may be inadequate as requiring too much processing or storage resources, while providing inaccurate text correction. Manufacturers and retailers of mobile devices would benefit greatly from the ability to offer devices with accurate and resource efficient text correction. Additionally, users of these mobile devices would benefit greatly by having a reduced frequency of text errors.
It is therefore apparent that an urgent need exists for an improved system and method for character error correction that is both accurate and efficient. This solution would replace current character error correction techniques with a more accurate system with regards to mobile devices and reduced resource demands; thereby increasing effectiveness of error reduction in text input performed on a mobile device.